\section{Conclusion}

In this work, we have presented OpenMM-Python-Force,
a callback mechanism that seamlessly bridges molecular dynamics simulations
with machine learning model inference.
Our evaluation demonstrates that this approach is not only robust and
computationally efficient but also remarkably versatile in its applications.
The applications of this callback mechanism extend well
beyond its initial implementation with PyTorch and OpenMM,
encompassing both classical and ab initio molecular dynamics simulations.
We anticipate that this work will substantially reduce the technical barriers
for integrating various computational backends with MD simulations,
thereby accelerating progress in relevant fields of research.
